# Fuck the Algorithm: Conceptual Issues in Algorithmic Bias

arXiv: https://arxiv.org/abs/2505.13509 · HTML: https://mlsanity.com/paper/2505.13509/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 35 references that are in the map.
- First posted on arXiv 16 May 2025.

## Builds on (most cited first, 6 of 6)

- [Exploring Author Gender in Book Rating and Recommendation](https://mlsanity.com/paper/1808.07586/) (2018)
- [Quantifying Search Bias: Investigating Sources of Bias for Political Searches in Social Media](https://mlsanity.com/paper/1704.01347/) (2017)
- [Responses to Critiques on Machine Learning of Criminality Perceptions (Addendum of arXiv:1611.04135)](https://mlsanity.com/paper/1611.04135/) (2016)
- [Bias Disparity in Recommendation Systems](https://mlsanity.com/paper/1811.01461/) (2018)
- [Revisiting Popularity and Demographic Biases in Recommender Evaluation and Effectiveness](https://mlsanity.com/paper/2110.08353/) (2021)
- [Epistemic values in feature importance methods: Lessons from feminist epistemology](https://mlsanity.com/paper/2101.12737/) (2021)

## Abstract (arXiv)

Algorithmic bias has been the subject of much recent controversy. To clarify what is at stake and to make progress resolving the controversy, a better understanding of the concepts involved would be helpful. The discussion here focuses on the disputed claim that algorithms themselves cannot be biased. To clarify this claim we need to know what kind of thing 'algorithms themselves' are, and to disambiguate the several meanings of 'bias' at play. This further involves showing how bias of moral import can result from statistical biases, and drawing connections to previous conceptual work about political artifacts and oppressive things. Data bias has been identified in domains like hiring, policing and medicine. Examples where algorithms themselves have been pinpointed as the locus of bias include recommender systems that influence media consumption, academic search engines that influence citation patterns, and the 2020 UK algorithmically-moderated A-level grades. Recognition that algorithms are a kind of thing that can be biased is key to making decisions about responsibility for harm, and preventing algorithmically mediated discrimination.